System and method for predicting future prices of a cut meat

ABSTRACT

A system and method for determining a future price of a selected meat cut type (MCT) of an animal for a selected future time period (FTP) including: receiving the selected MCT; storing said selected MCT in a memory; using a price model configured for determining said future price of said selected MCT for the selected FTP, said future price based on one or more defined risk levels, historical market price of said selected MCT for one or more time periods prior to said selected FTP, and live animal futures defining a price of the live animal traded as a commodity; determining via the model the future price of the selected MCT for the selected FTP, a price premium for the selected MCT for the selected FTP, and a hedge relationship defining the relative price of the live animal futures with said future price for the selected FTP; and sending the future price and the price premium for the selected MCT for the selected FTP for presentation on a user interface.

BACKGROUND OF THE INVENTION

The present invention relates to predicting the future prices ofcommodities, and more particularly to predicting the future prices ofcuts of meat.

Grocers, restaurants and other entities that are purchasers of meatgenerally buy individual cuts of meat from packers and processors whohandle the slaughtering, processing and distribution of the finishedproduct. A foremost concern of meat purchasers is the stability in theprice that they pay for a specific cut of meat. It is the practice ofsome meat purchasers, such as restaurants, to establish a set price foreach meal offered on a menu for a period of time. It is undesirable tochange the menu price frequently because consumers expect pricestability and expect to pay the same amount of money for the same meal.Other meat purchasers such as grocers and some packers and processorsalso face variable input costs in the price of cuts of meat and preferto avoid passing volatile input costs onto their customers wheneverpossible. Meat purchasers have several options in managing theirpurchases of cuts of meat. A meat purchaser may choose to buy a quantityof a certain cut of meat on a day-to-day basis when their inventory ofthe cut of meat desired falls below a certain level. This processintroduces a significant amount of risk into the purchaser's businessbecause of the day-to-day fluctuation in the price of a cut of meat.This type of risk is referred to as market risk.

Additionally, a purchaser may attempt to predict the future price of aspecific cut of meat based on the historical relationship between thespecific cut and the price of futures contracts for live cattle. As anexample, it is known in the art that, although many factors go into theprice of a specific beef product, none is more dominant than the priceof cattle. The traditional industry practice in forecasting prices of acut of meat has been to develop a ratio between the historic cash priceof a cut of meat and the relevant live cattle futures value. Once thisratio has been determined, price forecasts are calculated by applyingthe ratio to the current market price of live cattle futures contracts.This method of modeling has known disadvantages, including for example,an introduction of bias as the model is forced through an unobserved 0value. Therefore, the forecasts driven by this ratio model will carrybiases forward through time as the model predicts future prices of a cutof meat. There is also risk that the predicted prices from the ratiomodel will not accurately reflect the market price of the selected cutover time. Additionally, meat purchasers generally do not have theexpertise necessary to accurately forecast future prices of cuts ofmeat, as this is not part of their core business.

SUMMARY OF THE INVENTION

Accordingly, it is an object to provide a system and method fordetermining futures prices of a meat cut to obviate or mitigate at leastone of the above-mentioned disadvantages.

According to a first aspect, there is provided a method for determininga future price of a selected meat cut type (MCT) of an animal for aselected future time period (FTP), the method including the stepsimplemented on a computer processor of: receiving the selected MCT;storing said selected MCT in a memory; using a price model configuredfor determining said future price of said selected MCT for the selectedFTP, said future price based on one or more defined risk levels,historical market price of said selected MCT for one or more timeperiods prior to said selected FTP, and live animal futures defining aprice of the live animal traded as a commodity; determining via themodel the future price of the selected MCT for the selected FTP, a pricepremium for the selected MCT for the selected FTP, and a hedgerelationship defining the relative price of the live animal futures withsaid future price for the selected FTP; and sending the future price andthe price premium for the selected MCT for the selected FTP forpresentation on a user interface.

According to another aspect, there is provided a system for determininga future price of a selected meat cut type (MCT) of an animal for aselected future time period (FTP), the system including: a computerprocessor; a receipt module for receiving said selected MCT; a memoryfor storing said selected MCT; a predictor module configured for using aprice model to determine said future price of said selected MCT for theselected FTP, said future price based on one or more defined risklevels, historical market price of said selected MCT for one or moretime periods prior to said selected FTP, and live animal futuresdefining a price of the live animal traded as a commodity; the predictormodule further configured for determining via the price model the futureprice of the selected MCT for the selected FTP, a price premium for theselected MCT for the selected FTP, and a hedge relationship defining therelative price of the live animal futures with said future price for theselected FTP; and a presentation module for sending the future price andthe price premium for the selected MCT for the selected FTP forpresentation on a user interface.

DESCRIPTION OF FIGURES

FIG. 1 is a schematic of a data processing system that includes aportfolio management tool;

FIG. 2 is a diagram of an exemplary electronic device used forinteracting with the portfolio management tool of FIG. 1;

FIG. 3 is a component diagram of an embodiment of the portfoliomanagement tool of FIG. 1;

FIG. 4 shows the membership levels of different users of the portfoliomanagement tool of FIG. 3;

FIG. 5 is an exemplary log-in screen of the portfolio management tool ofFIG. 3;

FIG. 6 shows a model module of the portfolio management tool FIG. 3;

FIG. 7 is a flow-chart of steps performed by the model module of FIG. 6;

FIG. 8 is a block diagram of a model of the tool of FIG. 3;

FIG. 9 shows a predictor module of the portfolio management tool of FIG.3;

FIG. 10 is a flow-chart of the steps performed by the predictor moduleof FIG. 9;

FIG. 11 a is an exemplary prediction table displayed to a user of theportfolio management tool of FIG. 1;

FIG. 11 b is an alternative exemplary prediction table displayed to auser of the portfolio management tool of FIG. 1;

FIG. 12 a is an exemplary visual representation displayed to a user ofthe portfolio management tool of FIG. 1;

FIG. 12 b is an alternative exemplary visual representation displayed toa user of the portfolio management tool of FIG. 1;

FIG. 13 shows a purchasing module of the portfolio management tool ofFIG. 3;

FIG. 14 is a flow-chart of the steps performed by the purchasing moduleof FIG. 13;

FIG. 15 is an alternative embodiment of a portfolio management tool ofFIG. 1;

FIG. 16 shows an aggregate portfolio of the provider of the portfoliomanagement tool of FIG. 15;

FIG. 17 is an optimization module of the portfolio management tool ofFIG. 15;

FIG. 18 is an adjustment module of the portfolio management tool of FIG.15;

FIG. 19 is a flow-chart of the steps performed by the optimizationmodule and the adjustment module of the portfolio management tool ofFIG. 15; and

FIG. 20 is a table illustrating the net financial benefit provided bythe tool of FIG. 15;

DESCRIPTION OF EMBODIMENTS Data Processing System 10

Referring to FIG. 1, a data processing system 10 is presented forelectronically predicting, quoting, managing and securing forward pricesof cuts of meat, for example in a portfolio. Real-time derivative data,such as the current prices of live animal (e.g. cattle, hogs, etc.)futures contracts are provided to the electronic portfolio managementtool 12 by a derivative data provider 18, such as the Chicago MercantileExchange (CME), via the network 16. A futures contract is a standardizedcontract, traded on a securities exchange 310, to buy or sell a quantityof a specified commodity (such as live cattle) at a specified date inthe future, at a specified price. The future price is determined by theinstantaneous equilibrium between the forces of supply and demand amongcompeting buy and sell orders on the exchange 310 at the time of thepurchase or sale of the contract. The future date is called the deliverydate or final settlement date. The official price of the futurescontract at the end of a day's trading session on the exchange 310 iscalled the settlement price for that day of business on the exchange310. Periodically, the portfolio management tool 12 receives (eitherautomatically or by request) prices (e.g. averaged) of a specific cut ofmeat from a meat data provider 20, such as the daily prices reported forthe specific cut of meat by the United States Department of Agriculture(USDA). The portfolio management tool 12 communicates with applications14 to generate a dynamic and interactive visual representation 28 thatis presented to the member 8. As will be appreciated, applications 14may be distributed across a public or private network 22 or may resideon the same electronic device 18 (not shown) of the electronic portfoliomanagement tool 12. It will also be appreciated that the meat dataprovider 20 and the derivative data provider 18 may be accessible by thetool 12 via the network 16 as shown, or via a direct communicationslink. The tool 12 is operable to access a trading system 308 for buyingand selling futures contracts on a futures market 310, such as the CME.The trading system 308 may be an electronic component of a futuresmarket 310 or may be an electronic service offered by a third party forexecuting instructions related to the buying and selling of securities.

In one embodiment of the invention, the customer or the member 8 can usevarious electronic or digital devices 18, such as but not limited tocell phones, mobile computers, home computers, pagers and PDAs, tointeract with the portfolio management tool 12. For example, the member8 may choose to view predicted prices (i.e. in a future time period ascompared to the present time period in which the prices are viewed) andsecure predicted prices of a particular cut of meat. The member 8communicates with the portfolio management tool 12 by sending requests 9via the network 16 that are generated when the member 8 interacts with avisual interface 28 (such as but not limited to a web page) that ispresented to the member 8 on the digital device 18. As will beappreciated, in one embodiment, the portfolio management tool 12 may bein communication with applications 14 to process requests 9 submitted bythe member 8 and to dynamically generate and update the visualrepresentation 28 that is presented to the user 8. As shown, theportfolio management tool 12 is operable to access data in the tables 24and to store data in the tables 24. The tables 24 are in electroniccommunication with the tool 12. It will be appreciated that the networks16, 22 may include public or private networks, a group of wirelessand/or wired networks or any other medium that facilitates communicationbetween electronic devices 18.

Electronic Device 18

Referring to FIG. 2, the generic electronic device 18 can include inputdevices 38, such as a keyboard, microphone, mouse and/or touch screen bywhich the member 8 interacts with the visual interface 28. It will alsobe appreciated that the tool 12 resides on an electronic device 18 whichmay include similar components to the electronic device 18 employed bythe user 8. A processor 42 can co-ordinate through applicable softwarethe entry of data and requests 9 into the memory 40 and then display theresults on a screen as visual representation 28. A storage medium 46 canalso be connected to device 18, wherein software instructions and/ormember data is stored for use by the tool 12. As shown, the storagemedium 46 includes tables 24 wherein member data is stored as well asdata received from the derivative data provider 18 and the meat dataprovider 20.

The software instructions can comprise code and/or machine readableinstructions for implementing predetermined functions/operationsincluding those of an operating system, the tool 12, or otherinformation processing system, for example, in response to commands orinputs provided by a user 8 of the tool 12. The processor 42 (alsoreferred to as module(s) for specific components of the tool 12) as usedherein is a configured device and/or set of machine-readableinstructions for performing operations as described by example above.

As used herein, the processor/modules in general may comprise any one orcombination of, hardware, firmware, and/or software. Theprocessor/modules act upon information by manipulating, analyzing,modifying, converting or transmitting information for use by anexecutable procedure or an information device, and/or by routing theinformation with respect to an output device. The processor/modules mayuse or comprise the capabilities of a controller or microprocessor, forexample. Accordingly, any of the functionality provided by the systemsand processes of FIGS. 1-20 may be implemented in hardware, software ora combination of both. Accordingly, the use of a processor/modules as adevice and/or as a set of machine readable instructions is hereafterreferred to generically as a processor/module for sake of simplicity.

It will be understood by a person skilled in the art that the memory 40storage described herein is the place where data is held in anelectromagnetic or optical form for access by a computer processor. Inone embodiment, storage 40 means the devices and data connected to thecomputer through input/output operations such as hard disk and tapesystems and other forms of storage not including computer memory andother in-computer storage. In a second embodiment, in a more formalusage, storage 40 is divided into: (1) primary storage, which holds datain memory (sometimes called random access memory or RAM) and other“built-in” devices such as the processor's L1 cache, and (2) secondarystorage, which holds data on hard disks, tapes, and other devicesrequiring input/output operations. Primary storage can be much faster toaccess than secondary storage because of the proximity of the storage tothe processor or because of the nature of the storage devices. On theother hand, secondary storage can hold much more data than primarystorage. In addition to RAM, primary storage includes read-only memory(ROM) and L1 and L2 cache memory. In addition to hard disks, secondarystorage includes a range of device types and technologies, includingdiskettes, Zip drives, redundant array of independent disks (RAID)systems, and holographic storage. Devices that hold storage arecollectively known as storage media.

Referring to FIG. 2 b, the portfolio management tool 12 resides on andis implemented by one or more generic electronic devices 18. Genericdevice 18 may be a server that makes available the tool 12 to the member8 over the network 16. As known, device 18 may include input devices 38,such as a keyboard, microphone, mouse and/or touch screen by which theprovider of the tool 12 interacts with the tool 12 via the visualinterface 28. A processor 42 can co-ordinate through applicable softwarethe entry of data and requests 9 into the memory 40 and thendisplay/present the results on a screen as visual representation 28. Itwill be understood that the visual representation 28 displayed to theprovider of the tool 12 may be different than the visual representationdisplayed to a member 8. Further, it is recognised that the visualrepresentation 28 can be presented (as a result of operation of the tool12) to the member 8 on their client (e.g. of the tool 12 implemented ona networked server) electronic device 18 via the network 16. A storagemedium 46 can also be connected to device 18, wherein softwareinstructions, applications 14, member data, and other data is stored foruse by the tool 12. As shown, the storage medium 46 includes tables 24wherein member data is stored as well as data received from thederivative data provider 18, the meat data provider 20, and the exchange310 amongst other information.

The software instructions may comprise code and/or machine readableinstructions for implementing predetermined functions/operationsincluding those of an operating system, the tool 12, or otherinformation processing system, for example, in response to commands orinputs provided by a user 8 and/or the provider of the tool 12. Theprocessor 42 (also referred to as module(s) for specific components ofthe tool 12) as used herein is a configured device and/or set ofmachine-readable instructions for performing operations as described byexample above. Some or all of the modules of the tool 12 may bedistributed across a network as applications 14 or reside on theelectronic device 18. As is understood, some or all of the modules ofthe tool 12 may also be downloadable to the electronic device 18 of themember 8 and will be in communication with other modules of the tool 12on the electronic device 18 of the provider of the tool 12.

As used throughout, the processor/modules on the device 18 of the tool12 in general may comprise any one or combination of, hardware,firmware, and/or software. The processor/modules act upon information bymanipulating, analyzing, modifying, converting or transmittinginformation for use by an executable procedure or an information device,and/or by routing the information with respect to an output device. Theprocessor/modules may use or comprise the capabilities of a controlleror microprocessor, for example. Accordingly, any of the functionalityprovided by the systems and processes of FIGS. 1-20 may be implementedin hardware, software or a combination of both. Accordingly, the use ofa processor/modules as a device and/or as a set of machine readableinstructions is referred to generically as a processor/module for sakeof simplicity.

It will be understood by a person skilled in the art that the memory 40of the electronic device 18 of the tool 12 described herein is the placewhere data is held in an electromagnetic or optical form for access by acomputer processor. In one embodiment, storage 40 means the devices anddata connected to the computer 18 through input/output operations suchas hard disk and tape systems and other forms of storage not includingcomputer memory and other in-computer storage. In a second embodiment,in a more formal usage, storage 40 is divided into: (1) primary storage,which holds data in memory (sometimes called random access memory orRAM) and other “built-in” devices such as the processor's L1 cache, and(2) secondary storage, which holds data on hard disks, tapes, and otherdevices requiring input/output operations. Primary storage can be muchfaster to access than secondary storage because of the proximity of thestorage to the processor or because of the nature of the storagedevices. On the other hand, secondary storage can hold much more datathan primary storage. In addition to RAM, primary storage includesread-only memory (ROM) and L1 and L2 cache memory. In addition to harddisks, secondary storage includes a range of device types andtechnologies, including diskettes, Zip drives, redundant array ofindependent disks (RAID) systems, and holographic storage. Devices thathold storage are collectively known as storage media.

Database 24

A database or tables 24 is a further embodiment of memory 40 as acollection of information that is organized so that it can easily beaccessed, managed, and updated. In one view, databases can be classifiedaccording to types of content: bibliographic, full-text, numeric, andimages. In computing, databases are sometimes classified according totheir organizational approach. As well, a relational database is atabular database in which data is defined so that it can be reorganizedand accessed in a number of different ways. A distributed database isone that can be dispersed or replicated among different points in anetwork. An object-oriented programming database is one that iscongruent with the data defined in object classes and subclasses.

Computer databases 24 typically contain aggregations of data records orfiles, such as sales transactions, product catalogs and inventories, andcustomer profiles. Typically, a database manager provides users thecapabilities of controlling read/write access, specifying reportgeneration, and analyzing usage. Databases and database managers areprevalent in large mainframe systems, but are also present in smallerdistributed workstation and mid-range systems such as the AS/400 and onpersonal computers. SQL (Structured Query Language) is a standardlanguage for making interactive queries from and updating a databasesuch as IBM's DB2, Microsoft's Access, and database products fromOracle, Sybase, and Computer Associates.

Memory 40 storage is the electronic holding place for instructions anddata that the computer's microprocessor 42 can reach quickly. When thecomputer 18 is in normal operation, its memory 40 usually contains themain parts of the operating system and some or all of the applicationprograms and related data that are being used. Memory 40 is often usedas a shorter synonym for random access memory (RAM). This kind of memoryis located on one or more microchips that are physically close to themicroprocessor in the computer.

Portfolio Management Tool 12

Reference is next made to FIG. 3, which shows a component diagram of aportfolio management tool 12. As shown, the tool 12 includes a receiptmodule 30 for managing user requests 9 and for directing user requests 9to one or more of the appropriate modules 32, 200, 300 and/or 560. Asthe member 8 interacts with visual representation 28 via their inputdevices 38, the receipt module 30 co-ordinates the responsibilities andtasks of the other modules 32, 200, 300 and/or 560 of the managementtool 12. The management tool 12 communicates with a data manager 34which is operable to retrieve data from and send data to the tables 24upon instruction from the receipt module 30 and/or the other componentsof the tool 12. The data manager 34 is also in electronic communicationwith the derivative data provider 18 and the meat data provider 20. Thevisual manager/module 36 is instructed by the components of themanagement tool 12 to recreate and redraw the visual representation 28for viewing and further interaction by the user or member 8. It isrecognised that the visual manager/module 36 can present therepresentation 28 on the display of the tool 12 and/or send therepresentation 28 over the network 16 for presentation on the display ofthe member's networked device 18

The tool 12 includes a Model Module 560 for creating particular model(s)204 of the portfolio management tool 12. As described below, the modelmodule 560 is operable to create a model 204 for each meat cut (e.g. andfor associated delivery time period—e.g. month) that is offered to themember 8 by the tool 12. The model module 560 instructs the data manager34 to store each model 204 in the tables 24 or directly into memory 40of the electronic device 18 for use by the other components of the tool12. The tool 12 also includes a Predictor Module 200 which is operableto generate the future predicted prices of a cut of meat for a selectedfuture time period upon selection by the user 8. The predictor module200 is in communication with and retrieves the appropriate model 204from the model module 560 (or directly from tables 24 or memory 40). Thepredictor module 200 uses the model 204 to create a prediction table 220and instructs the visual manager 36 to render the prediction table 220to the visual interface 28 for viewing and interaction by the member 8(either locally and/or remotely to the tool 12). The tool 12 alsoincludes a Purchasing Module 300 for buying and selling live animalfutures contracts on a futures market 310. The purchasing module 300retrieves a hedge ratio 906 from the model module 560 and determines thenumber of live animal futures to buy. Once the number of live futures isdetermined by the purchasing module 300, components of the purchasingmodule buy the number of live futures and store the informationregarding the trade in the tables 24 via the data manager 34. The tool12 also includes a subscription manager/module 32 for managing themember accounts of the members 8 and for instructing the visual manager36 the information to display to the member 8 and the types ofinteraction that the member 8 is allowed to perform with the tool 12.

Subscription Manager/Module 32

Referring next to FIG. 4, it is shown that the tool 12 enables members 8a, 8 b to have different membership levels that are obtained from theprovider of the portfolio management tool 12 through a subscription ormembership. By different membership levels, it is meant that members 8a, 8 b are able to view different information and interact differentlywith the portfolio management tool 12. As shown, member 8 a interactswith the visual interface 28 a on electronic device 18 a. The member 8 ais allowed to view a prediction table 220 a for a selected cut of meatfor the selected delivery period; however, the user 8 a is unable tosecure the forward price for the selected cut for a selected deliveryperiod, because the member 8 a does not have the required permission tosecure forward prices. In addition, the member 8 a is able to see therisk level associated with a predicted price of the selected cut ofmeat, but the member 8 a is not able to see the premium that would becharged to the member 8 a to eliminate this risk. Contrarily, the member8 b is able to view the predicted price of the selected cut, and is alsoable to secure the forward price for a desired quantity of the selectedcut for a chosen delivery month. The member 8 b is able to view the risklevels associated with the predicted future prices for the selected cutand is also able to see the premium that is charged to the member 8 b inexchange for the provider of the tool 12 eliminating the risk (i.e. bythe provider securing forward prices of the selected cut for the member8 b). As shown, the tool 12 is in communication with the futures market310 and an associated trading system 308. When the member 8 b requeststo secure forward prices, the tool 12 buys a certain number of livecattle futures contracts by accessing the trading system 308 to protectthe provider of the tool 12 against market price movements in theparticular cut of meat selected. It will be appreciated that the tool 12is capable of having any number of membership levels for each member 8a, 8 b that has permission to access the tool 12. For example, the tool12 may allow one member 8 a to secure prices for a selected cut of meat,and allow another member 8 b (or the same member 8 a) to secure pricesfor another commodity that is associated with a futures contract (e.g.pork bellies) that is traded on a public market 310.

Logging on to the Portfolio Management Tool 12

Reference is next made to FIG. 5 which illustrates an exemplary log-inscreen 360 for interacting with the electronic portfolio management tool12. The log-in screen 360 may be a web-page that is displayable on thedevice 18. The member 8 accesses the log-in screen 360 in known mannerby employing a web-browser and instructing the web-browser to displaythe log-in screen 360 (for example, by typing the web-address of thelog-in screen 360 into the browser or by performing a web search for theprovider of the tool 12). As shown, the log-in screen 360 includes auser ID field 362 for receiving the user ID from the member 8, apassword field 364 for receiving a password from the member 8, and asubmit button 366 for submitting the user ID and password to theportfolio management tool 12 and for gaining access to the portfoliomanagement tool 12 if the member 8 has permission. To gain access to theportfolio management tool 12, the member 8 must enter a preselected userID into the user ID field 362 and a preselected password into thepassword field 364. As will be appreciated, the user ID and the passwordare unique to the member 8. Once the user ID and password are entered,the member 8 selects the submit button 366. The interactions of the user8 are processed by the receipt module 30. The receipt module 30 capturesthe user ID and password entered and communicates the data to thesubscription manager 32 which is operable to determine if the user IDand password are valid and to manage the information and features thatare available to the member 8. The subscription manager 32 uses theentered user ID and password to construct a query and requests a dataset from the data manager 34. The data manager 34 processes the queryand returns a data set to the subscription manager 32 that correspondsto the criteria of the query. The subscription manager 32 analyzes thedata set and instructs the visual manager 36 to generate a visualrepresentation 28 for display as a web page. If the subscription manager32 determines that the member 8 has permission to access the tool 12,the subscription manager 32 instructs the visual manager 36 to display awelcome screen as a web page to the member 8 and to display certaincontrols on the visual representation 28 that correspond to thepermission level of the member 8. Contrarily, if the subscriptionmanager 32 determines that the member 8 does not have permission toaccess the tool 12 (or determines that the password and/or the user IDare incorrect), the subscription manager 32 instructs the visual manager36 to display a web-page to the user 8 which informs the user 8 that thepassword and/or user ID are not recognized by the tool 12.

Model Module 560

Reference is next made to FIG. 6, which illustrates the model module 560of the portfolio management tool 12. The model module 560 is operable tocreate one or more model(s) 204 for each cut of meat and associateddelivery future time period for which the provider of the tool 12 willsecure forward prices (e.g. predicted future prices) to a member 8. Eachmodel 204 is operable to predict the forward/future price of aparticular cut of meat based on the historical relationship between themarket prices of the particular cut of meat and the prices of liveanimal futures contracts for the animal from which the cut of meat isderived. The model module 560 creates each model 204 by employing thesteps outlined in FIG. 7, and instructs the data manager 34 to storeeach model 204 in the tables 24 or into memory 40. When a user 8requests a prediction for a particular cut of meat, the receipt module30 instructs the model module 560 to create a model 204 that representsthe selected cut of meat. In one embodiment, the model module 560retrieves the appropriate model 204 from the data manager 34. If theparticular model 204 does not exist (i.e. it has not been created yet)the model module 560 creates a new model 204 by performing theoperations illustrated in FIG. 7. If the particular model 204 exists,the model module 560 may update the model 204 using data from the datamanager 34 that was not previously applied to the model 204.Alternatively, the model module 560 may determine that the model 204 isup-to-date and will instruct the receipt module 30 that the model 204 isready for use by the other components of the tool 12.

Reference is next made to FIG. 7, which illustrates the series of stepsthat the model module 560 performs to create each prediction model 204of the tool 12. As mentioned above, each cut of meat can have a model204 that is created for each delivery future time period. At step 562,the model module 560 is provided for representing the selected cut ofmeat and the selected delivery period from the receipt module 30. Themodel module 560 retrieves the historic prices of the selected cut fromthe meat data provider 20 via the data manager 34 at step 564. At step566, the model module 560 retrieves the historic prices of live animalfutures contracts from the derivative data provider 18. It will beappreciated that steps 564 and 566 may be performed in parallel orsequentially. Next, at step 568, the model module 560 performs a curvefitting function (such as a regression analysis) on the historic pricesand creates a curve of best fit which is stored in memory or in thetables 24 for further use by the tool 12. At step 570, the model module560 retrieves error terms 206 from other models 204 for the selectedcut, but for different delivery periods. For example, the model module560 may retrieve error terms 206 for models 204 that correspond to thesame cut, but for one month and/or two months before the delivery perioddesired by the user 8. At step 572, the module 560 modifies the curve ofbest fit to give the curve greater predictive abilities by applying theerror terms 206 as properties of the curve. At step 574, the modelmodule 560 calculates the hedge ratio 906 for the selected cut which isused by the Purchasing Module 300, as is described below. Finally, atstep 576, the model module 560 stores the curve as a model object 204 inmemory 40 and/or in the tables 24 for further use by the tool 12.

In one embodiment, the prediction model 204 is an ordinary least squaresregression model to predict future prices, though it will be appreciatedthat the portfolio management tool 12 may implement any model 204 thatpredicts the future prices of a selected cut of meat based on theavailable input data (i.e. the historic reported market prices of aproduct and the historic market prices of a security that relates to theproduct). An ordinary least squares regression model 204 is a method offinding the best curve fit for a data set, where the best fit is thatinstance of the model 204 for which the sum of the squared residuals isminimized. The residuals are the differences between an observed valueand the value given by the model 204. In one embodiment, the model 204is also self-correcting. The model module 560 compares the predictedprice of a selected cut for each delivery period to the actual marketprice of the selected cut for the same period. The market price of theselected meat cut is provided to the module 560 from the data manager 34which accesses the meat data provider 20. The module 560 calculates thedifference between the predicted price and the market price of theselected cut (i.e. the error terms 206 in the predicted price) andmodifies the internal structure of the model 204 by fitting a new curvefor the model 204. In an embodiment, the module 560 fits a new curve forthe model 204 on a real-time basis, as the market price of a selectedcut of meat is available from the meat data provider 20. In yet anotherembodiment, the module 560 fits a new curve for model 204 at apre-determined frequency (such as a month-by-month basis) by minimizingthe squares of the error terms 206 that have become available since thelast curve fit performed by the module 560 for the model 204.

In one embodiment, the model 204 takes the residuals (i.e. errors) fromother models 204 a,b as inputs. Such a model 204 is a hybridauto-regressive model because lagged residual terms from models 204,a,bcorresponding to previous months for the same cut of meat are includedas a characteristic of the model 204. An autoregressive trend is one inwhich current results are affected by past results. Although theresiduals are generally white noise (i.e. containing no predictiveinformation), lagged residual terms from the models 204 a,b for previousmonths are reintroduced into the model 204 to form a hybridauto-regressive multivariate model 204. The inclusion of residuals frommodels 204 a,b further develops the predictive ability of the model 204by allowing the residuals from previous months to direct predictions forthe price of the selected cut in the future. In this embodiment,inputting residuals into model 204 from models 204 a,b that correspondto delivery months immediately before the delivery month for the samecut (as selected by the member 8) gives the model 204 greater predictiveaccuracy. The ordinary least squares regression analysis of the model204 can be represented by the following equation:

Model:  Y = β X With  solution  β̂ = (X^(T)X)⁻¹(X^(T)Y)${{In}\mspace{14mu} {matrix}\mspace{14mu} {notation}{\text{:}\begin{bmatrix}{\hat{\beta}}_{0} \\{\hat{\beta}}_{1} \\\vdots \\{\hat{\beta}}_{1}\end{bmatrix}}} = {\begin{bmatrix}N & {\sum\; X_{1,i}} & \ldots & {\sum\; X_{k,i}} \\{\sum\; X_{1,i}} & {\sum\; X_{1,i}^{2}} & \ldots & {\sum\; {X_{1,i}X_{k,i}}} \\\vdots & \vdots & \ldots & \vdots \\{\sum\; X_{k,i}} & {\sum\; {X_{1,i}X_{k,i}}} & \ldots & {\sum\; X_{k,i}^{2}}\end{bmatrix}^{- 1}\begin{bmatrix}{\sum\; Y_{i}} \\{\sum\; {Y_{1}X_{1}}} \\\vdots \\{\sum\; {Y_{k}X_{k}}}\end{bmatrix}}$

In the above equations, Y can represents the observed historical pricesof the selected cut of meat for the selected delivery period, Xrepresents a matrix containing the intercept, the value of live futurescontracts and at least one residual error term, and {circumflex over(β)} represents the vector of coefficient estimates. The vector ofcoefficient estimates contains the set of coefficients which detail howthe predicted cash price is to be calculated given the observed data.The only variable in the above model 204 that is not controlled by theprovider of the tool 12 is the value of the live animal futurescontract.

Reference is next made to FIG. 8, which illustrates in black-box formthe inputs that are received by a model 204 and the outputs that areproduced by the model 204 when the model 204 is employed by thecomponents of the tool 12. As mentioned above, the model 204 implementsa linear regression analysis to predict the future price of a specificcut of meat for a selected future period. In its internal structure, themodel 204 includes a curve 904 and a hedge relationship (e.g. ratio)906. The curve 904 and the hedge ratio 906 are characteristics of themodel 204 that are created by the model module 560 as described above.The hedge ratio 906 is determined by the model module 560 by analyzingthe historic live cattle futures prices provided by the derivative dataprovider 18 and historic prices for the selected cut of meat provided bythe meat data provider 20. The hedge ratio 906 is then used by thepurchasing module 300 to determine the number of live futures to buy tominimize the risk of the provider of the tool 12 when the providersecures a future price for a selected cut of meat to the member 8. In anembodiment, the hedge ratio 906 is a ratio of the relative movements inthe prices of the live cattle futures and the reported prices of theselected cut of meat. For example, the model module 560 may determinethat for every three dollar increase in the price of life cattlefutures, the price of a selected cut of meat historically increases byone dollar. Likewise, the model module 560 may determine that for everythree dollar decrease in the price of live cattle futures, the selectedcut of meat historically decreases by one dollar in price. In thisembodiment, the provider of the tool 12 may wish to buy three livecattle futures for each unit of the selected cut when the provider ofthe tool 12 secures a forward price for the member 8.

As shown in FIG. 8, the model 204 takes real-time live futures prices908 as an input, which are provided by the data manager 34 whichretrieves the live futures prices 908 from the derivative data provider18. The live futures prices 908 are used by the curve 904 to predict thefuture predicted prices 910 of the selected cut for the selecteddelivery period. The model 204 may also input at least one residual 206as an input term as described above. The residuals 206 may be errorterms for the model 204 for previously generated predictions 910 in thedelivery month, or may be residuals 206 from other models 204 a,b forthe selected cut, but for previous delivery months. In an embodiment,the model 204 takes two residuals 206 a,b: one residual 206 a from amodel 204 a that corresponds to the selected cut but for a deliveryperiod one month before the selected delivery period, and a secondresidual 206 b from a model 204 b that corresponds to the selected cutbut for a delivery period two months before the selected deliveryperiod.

As shown, the model 204 also generates a value for the basis risk 912.The term “basis risk” refers to the risk faced by the provider of thetool 12 and the member 8 that the actual market outcomes (i.e. themarket price of a specific cut of meat) deviates from the future price910 predicted by the tool 12. The provider of the tool 12 assumes thebasis risk 912 in return for a customer paying a basis risk premium 916.The basis risk value 912 indicates the range of reasonably expectedoutcomes for analysis by the member 8 and the provider of the tool 12.The basis risk 912 is a monetary value based on a percentage of thepredicted price 910 of a cut of meat and is based on the basis riskconfidence level 914. The range is determined by the basis riskconfidence value 914 which is inputted to the model 204 by the providerof the tool 12. As an example, the provider of the tool 12 may set thebasis risk confidence level 914 to 97.5%. This means that the predictedprices 910 of the selected cut are expected to statistically fall withinthe range of the basis risk values 912 at a rate of 97.5% of the time.The provider of the tool 12 may set the basis risk confidence level 914to any level desired. When a member 8 subscribes to a “premium”membership level, the member 8 is allowed to view the basis risk values912 when they submit a prediction request as well as the basis riskpremium 916. Members 8 that do not subscribe to the “premium” membershiplevel are able to view the basis risk values 912 but not the associatedpremium 916 as these customers are not given permission to securepredicted prices in the future.

The model 204 also generates a basis risk premium 916. The basis riskpremium 916 is the amount of money that the member 8 can pay to theprovider for the provider of the tool 12 assuming the basis risk 912when the provider secures a predicted future price 910 for the selectedcut to the customer 8. The amount of the basis risk 912 (and hence theamount of the basis risk premium 916) varies by cut of meat and bymonth. The basis risk premium 916 is a monetary value that correspondsto a percentage of the predicted price 910 and is dependent on the basisrisk premium confidence level 918 that is chosen by the provider of thetool 12 that is an input to the model 204. For example, the provider ofthe tool 12 may set the basis risk premium confidence level 918 to 80%.It is understood that the predicted future price 910 is on a 50%confidence level since there is a 50% chance that the actual price willbe lower than the predicted price 910, and a 50% chance that the actualprice will be higher than the predicted future price 910. The differencebetween the basis risk premium confidence level 918 and the confidencelevel of the predicted price 910 determines the amount of the basis riskpremium 916. In the above example (i.e. where the basis risk premiumconfidence level 918 is set to 80%) the member 8 can pay a basis riskpremium 916 equal to a 30% risk off the forecasted price 910 and theprovider of the tool 12 will assume the basis risk 912 on behalf of thecustomer 8.

Although the basis risk confidence level 914 and the basis risk premiumconfidence level 918 are inputs to the model 204, is to be understoodthat these values are controllable by the provider of the tool 12. Forexample, the provider of the tool 12 may manage its internal risk inguaranteeing the forward prices of cuts of meat to customers 8 bymodifying the confidence levels 914, 918. The only uncontrollable inputto the model 204 is the price of live cattle futures 908 which gives theprovider of the tool 12 significant flexibility in managing its riskover time by modifying the confidence levels 914, 918.

Predictor Module 200

Reference is next made to FIG. 9, which illustrates the Predictor Module200 of the tool 12. The Predictor Module 200 is operable to predictfuture prices 910 for a selected cut of meat for a selected deliveryperiod as chosen by the member 8. For example, the member 8 may wish toview the predicted prices 910 of a rib-eye steak for every month in thenext 12-month period. Alternatively, the member 8 may wish to view thepredicted price 910 of a pound of ground beef for a specific month, forexample, December 2010. As shown, the Predictor Module 200 receives arequest by the receipt module 30 and retrieves data from the meat dataprovider 20 and the derivative data provider 18 via the data manager 34.The module 200 interfaces with a prediction model 204 which is a set ofinstructions for processing inputs to the model 204 and for generating aset of future prices 910 for the selected cut of meat. The module 200applies the data received from the provider 18 an input to the model 204and generates the basis risk 912, predicted future prices 910 and thebasis risk premium 916 as described above.

It will be understood that there is a risk that the prediction model 204will not perfectly predict the future price 910 of a selected cut ofmeat. When a member 8 chooses to secure a future price 910, the providerof the tool 12 assumes the risk (i.e. the basis risk 912) that thepredictions 910 will not be correct. The basis risk 912 will bedifferent for each selected cut of meat and for each delivery period. Inexchange for assuming the basis risk 912, the provider of the tool 12charges a basis risk premium 916, which is similar to an insurancepremium that is to be paid by the member 8. Generally, the basis riskpremium 916 is a fraction of the predicted price of a selected cut ofmeat, which gives the member 8 an opportunity to receive price stabilityfor an additional price premium.

When a user 8 requests a prediction 910 for a selected cut of meat for aselected delivery period, the receipt module 30 communicates theprediction request to the module 200 which employs the prediction model204. The member 8 selects the cut of meat and the delivery period byinteracting with controls on the user interface 28 and by submitting therequest to the portfolio management tool 12. The receipt module 30communicates the selected cut and delivery period to the module 200which accesses the appropriate prediction model 204 from the datamanager 34. The model 204 may implement an ordinary least squaresregression model, as described above, or the model 204 may implement anynumber of different numerical techniques. In one embodiment, the model204 may be interchangeable and customizable based on the wishes of themember 8 and/or the provider of the tool 12. The model 204 runs a set ofinstructions to generate prediction data and provides the predictiondata to the module 200. The module 200 communicates the prediction datato the receipt module 30 which instructs the visual manager 36 to renderthe prediction data in a prediction table 220 for further viewing andinteraction by the member 8. The module 200 may also communicatedirectly with the visual manager 36 to render the prediction table 220to the user interface.

The predictor module 200 may also have the capability to dynamicallygenerate predictions at a predetermined frequency. In an embodiment, thepredetermined frequency is customizable by the member 8. For example,the predictor module 200 may generate prediction data for the selectedcut and delivery period every minute, every second or on a real-timebasis. The module 200 retrieves the applicable input data from the datamanager 34 at the predetermined frequency and communicates the inputdata to the model 204. The model 204 processes the input data based on aset of instructions and creates prediction data which is communicated tothe module 200 for rendering as the prediction table 220 on the visualrepresentation 28.

Reference is next made to FIG. 10, which illustrates the series of stepsthe predictor module 200 carries out when a prediction request is madeby a member 8. At step 700, the predictor module 200 receives theprediction request via the receipt module 30. At step 702, the predictormodule 200 retrieves the appropriate model 204 for the selected cut anddelivery month via the data manager 34 or directly from memory 40. Atstep 704, the module 200 determines whether a model 204 was successfullyretrieved at the previous step 702. If a model 204 is successfullyretrieved, the module 200 generates prediction data 910 at step 708 forpresentation as the visual representation 28 by the visual manager 36.If the model 204 is not successfully retrieved at step 702, the module200 instructs the model module 560 to generate a new model 204 for theselected cut at step 706 and the module 200 goes back to step 702. Atstep 710, the prediction module 200 obtains the permission or membershiplevel of the member 8 from memory or the tables 24. Finally, at step712, the prediction module 200 instructs the visual manager 36 to createa prediction table 220 that is customized to the permission level of themember 8 and to render the prediction table 220 on the visual interface28 for viewing and possible further interaction by the member 8.

Prediction Tables 220

Reference is next made to FIG. 11 a, which illustrates a predictiontable 220 a which is displayable on the user interface 28. Theprediction table 220 a is operable to display the prediction datacreated by the prediction model 204 in a convenient format for analysisby the member 8. As shown, the prediction table 220 a includesprediction rows 222 and columns 224. Each row 222 represents theprediction price 910 for the selected cut of meat for each month 226 inthe selected delivery period and associated information. In theexemplary prediction table 220 a, the member 8 has requested to viewprediction data for each month from April 2009 until January 2010 byinteracting with controls (not shown) on the visual interface 28. Thepredictor module 200 instructs the model 204 to create prediction dataand communicates the prediction data to the visual manager 36 forrendering the results in the prediction table 220 a. The table 220 a isupdated by the visual manager 36 at the predetermined frequency aschosen by the member 8 or in real-time. As is apparent, the member 8 canview the following data: the delivery month 226, the price of livecattle futures contracts 908 for each month 226 in the delivery period,the predicted price 910 of the selected cut of meat for each month 226in the delivery period, and the daily change 229 in the predicted price910 for the selected cut of meat. The member 8 is restricted in whichdata is viewable on the visual representation 28 because the member 8does not have full membership privileges as described above withreference to FIG. 4. In an embodiment, the member 8 is able to viewadditional information such as the basis risk 912 without having apremium membership as shown in FIG. 12 a.

Reference is next made to FIG. 11 b, which illustrates an exemplaryprediction table 220 b presented to the member 8 on the visualrepresentation 28. As shown, the prediction table 220 b contains all ofthe information of the prediction table 220 a in FIG. 11 a, as well asother information that provides a valuable analytical tool to the member8. The member 8 makes a prediction request to the tool 12 by interactingwith controls (not shown) on the visual representation 28. Predictiondata is generated by the predictor module 200 by employing a predictionmodel 204 and is presented on the visual representation 28 as predictiontable 220 b by the visual manager 36. The prediction table 220 b alsoincludes information related to the basis risk 912 of the predictedprices 910 and the basis risk premium 916 (indicated by the text “BRP”)that the member 8 can choose to pay in exchange for securing the prices910 for any delivery month 226. In exchange for paying the basis riskpremium 916, the provider of the tool 12 assumes the financial risk forthe basis risk 912 associated with the predictions 910 (i.e. the riskthat the predicted future prices 910 are not correct). The predictiontable 220 b also includes the Up Market Risk 248 and the Down MarketRisk 250. As is apparent, the Up Market Risk 248 is the risk that themarket price of the selected cut will be higher than the predictedprices 910 by the amount of the basis risk 912 at prevailing (i.e.current) live cattle futures values. The Down Market Risk 250 is therisk that the market price of the selected cut will be lower than thepredicted prices 910 by the amount of the basis risk 912 at prevailing(i.e. current) live cattle futures values. The range between the UpMarket Risk 248 and the Down Market Risk 250 (e.g. 3.8991−3.2662=$0.663in row 241) is the risk that the provider of the tool 12 is willing toassume in exchange for the member 8 paying the associated basis riskpremium 916. As mentioned above, the provider of the tool 12 may adjustthe basis risk confidence level 914 to any desired level to increase ordecrease the risk represented by the range between the Up Market Risk248 and the Down Market Risk 250. It is recognised that the basis riskcan be determined by a defined comparative relationship, such as but notlimited to a difference.

Reference is made to FIG. 12 a, which shows a visual representation 28that is presented to a member 8 when the member 8 selects a cut of meatfor which to generate predicted future prices 910. As shown, the visualrepresentation 28 includes a prediction table 220 for displaying thepredicted prices 910 to the member 8, a selection menu 502 for allowingthe member 8 to select a cut of meat 506 for which to generate predictedfuture prices 910, and a chart 504 for allowing the member 8 to analysethe risk inherent in the predicted future prices 910. The member 8 hasselected the cut 506 from the selection menu 502, for example, byclicking on the cut 506 with a mouse cursor or using another inputdevice 38. Once the member 8 selects the cut 506, the receipt module 30instructs the predictor module 200 to generate the prediction table 220and the chart 504. As described above, the predictor module 200retrieves the input data via the data manager 34 and creates aprediction data set. The predictor module 200 then instructs the visualmanager 36 to create and display the visual representation 28 to themember 8. As shown, the chart 504 includes a prediction line 508 whichillustrates the predicted prices 910 of the selected cut 506 over thetime axis 510. Also, the chart 504 displays a risk band 512 to the user8 which represents the range of reasonably expected outcomes of thepredicted prices 910. The risk band is a visual representation of thebasis risk 912 in the predictions 910. As described above, the basisrisk 912 is defined by threshold values (i.e. the basis risk confidencelevel 914) that are customizable by the provider of the tool 12 and/orthe member 8. As an example, the risk band 512 may represent a 97.5%confidence level 914, meaning that based on the internal structure ofthe model 204 (which has been created to represent the relationshipbetween the historical prices of the selected cut 506 and the historicalprices of live cattle futures 908), the predicted future prices 910 willfall within the risk band 512 97.5% of the time. The basis risk 912 canbe modified to any confidence level 914 depending on the wishes of theprovider and/or the member 8.

Reference is next made to FIG. 12 b, which illustrates a visualrepresentation 28 which is presented to the member 8 when the member 8selects a cut of meat 506 for which to generate predictions of thefutures prices 910 of the selected cut 506. As shown, the chart 504includes a prediction line 508 for visualizing the predicted price 910of the selected cut 506 over the time axis 510. The chart 504 includes arisk band 512 for visualizing the range of reasonably expected futureprices and also includes an insurance premium line 514 that enables themember 8 to view how much the basis risk premium 916 will cost themember 8 if the member 8 wishes to secure the future price 910 of theselected cut 506. The basis risk premium 916 is determined by the model204 to be the difference (or other appropriate comparative relationship)between a basis risk premium confidence level 918 and the 50% confidencelevel of the predicted future prices 910. As described above, the basisrisk premium confidence level 918 is an input to the model 204 that maybe controlled by the provider of the tool 12. As an example, the basisrisk premium confidence level 918 can be set to 80% by the provider ofthe tool 12. This means that the provider has determined that there isan 80% probability that the predicted future price 910 of the selectedcut 506 will fall within the range represented by the basis risk premiumconfidence level 918. In exchange for paying the basis risk premium 916,the provider of the tool 12 assumes the risk that the predicted futureprices 910 will not fall within the basis risk premium confidence level918.

It is to be understood that in other embodiments of the invention, thetool 12 may show any or all of the information shown in the predictiontables 220 of FIGS. 11 a, 11 b and FIGS. 12 a, 12 b to a member 8. Forexample, the provider of the tool 12 may wish to allow all members 8 tosee the information in FIG. 12 b. However, for example in otherembodiments, the provider of the tool 12 may wish to display only thepredicted future prices 910 to a member 8.

Purchasing Module 300

Reference is next made to FIG. 13, which illustrates the PurchasingModule 300 of the Portfolio Management Tool 12. As shown, the purchasingmodule 300 includes a purchasing manager 302 for managing purchaserequests for a quantity of the selected cut of meat 506 and for managingthe buying and selling of live animal futures contracts throughout theduration of the purchasing contracts. The purchasing module 300 allows amember 8 to secure a price 910 for a quantity of a desired cut 506 ofmeat. The member 8 is provided the predicted price 910 by the providerof the portfolio management tool 12 for the duration of the contractdefined by the delivery period. A member 8 can initiate a purchaserequest by interacting with controls (not shown) on the visual interface28. The controls are operable to receive at least three types ofinformation from the user 8, namely, a cut of meat desired 506, thedelivery period and the quantity of the cut of meat. When the member 8initiates the purchase request (for example, by clicking on a submitpurchase request button (or by communicating instructions to theprovider of the tool 12), the receipt module 30 communicates the requestto the purchasing manager 302 for further processing. It will beappreciated that the member 8 may send written instructions in an emailor in another format, or may communicate instructions verbally with theprovider of the tool 12. Additionally, the member 8 may havepredetermined instructions for the provider of the tool 12 to takecertain actions on the happening of certain events, such as when thepredicted future prices 910 of a selected cut 506 are above or below apredetermined level (e.g. recorded in the memory of the tool 12). Thepurchasing manager 302 is in communication with the predictor module 200and requests specific information from the module 200. Specifically, thepurchasing manager 302 may request information relating to the number offutures contracts to buy to manage the risk associated with providingthe predicted price 910 of the selected cut 506 to the member 8. Themodel 204 provides the information as a hedge ratio/relationship 906 onrequest to the receipt module 30 which relays the information back tothe purchasing manager 302. In another embodiment, the information isstored in the tables 24 and the purchasing manager 302 is able toretrieve the information via the data manager 34. The purchasing,manager 302 is operable to execute trades (i.e. to buy and sell) futurescontracts on a trading system 308 of a futures market 310. The tradingsystem 308 may be provided by the operator of the futures market 310 ormay be a system provided by a third party as is known. Upon receipt ofthe number of futures contracts to buy (information provided by themodel 204 as described above) the purchasing manager 302 buys therequired number of futures contracts.

As described above, the prediction model 204 produces a hedge ratio 906,which represents the relationship between the movement in the marketprice of a futures contract and the associated movement in the price ofthe selected cut of meat 506. The hedge ratio 906 determines the amountof futures that the provider purchases to hedge against the riskassociated with providing the predicted future predicted price 910 ofthe selected cut 506 to the member 8. In one embodiment, the hedge ratio906 is expressed in pounds (or another unit of weight). As an example, aprovider of the tool 12 is willing to provide a future price to acustomer on 100,000 lbs of rib-eye steaks and the model module 560determines that the hedge ratio 906 of the model 204 (i.e. the model 204for rib-eye steaks for the selected month) to be 2.8. In this example,the purchasing module 300 will purchase a quantity of live cattlefutures contracts that corresponds to a weight of live cattle equal to2.8 times the weight of rib-eyes (i.e. the selected cut of meat). As isknown, there are 40,000 lbs of live cattle for every live cattle futurescontract. In this example, the purchasing module 300 buys 7 live cattlefutures contracts which correspond to 280,000 lbs of live cattle whichprovides a hedge ratio of 2.8 (i.e. 280,000 lbs of live cattle for100,000 lbs of rib-eye steaks).

In one embodiment, the purchasing module 300 includes a settlementmanager/module 304 for selling or “unwinding” the provider's portfolioof futures contracts. The settlement manager 304 is operable to sell thefutures contracts in an optimal fashion over the course of the deliverymonth. In an embodiment, the provider of the tool 12 buys live cattlefutures that have the closest settlement month that is after thedelivery month of the cut of meat. In practice, live cattle futures mayonly be available for even months (i.e. February, April, June, etc). Ifa customer 8 wants December rib-eyes, the provider buys futures forFebruary of the next year. If the customer wants to buy March rib-eyes,however, the provider buys April live cattle futures. In an embodiment,the settlement manager 304 sells the quantity of futures contracts in adistributed fashion in periodic (e.g. weekly) increments over the coursedelivery period (e.g. month) of the cut of meat (for example not thesettlement month of the live cattle futures). For example, if theprovider of the tool 12 is holding 16 futures contracts to hedge againstprice movements for a cut of meat that has a particular delivery month,the provider may choose to sell 4 futures each week during each week ofthe delivery month to minimize the risk of selling the futures at a lowpoint during the month. In another embodiment, the provider buys livecattle futures with a settlement month the same as the delivery month ofthe cut of meat, if available, and for the nearest settlement monthafter the delivery month if live cattle futures are not offered for theselected delivery month.

The purchasing module 300 may also include a contract manager/module 306for managing contracts between the member 8 and the provider of theportfolio management tool 12. As will be appreciated, each time a member8 chooses to secure the future prices 910 of a selected cut of meat 506,the provider of the tool 12 agrees to provide the predicted future price910 for the selected cut of meat and the selected delivery month whichcreates a contract between the member 8 and the provider of the tool 12.The contract manager 306 is operable to manage the recordation andexecution of the terms of the contract that is entered into between themember 8 and the provider of the tool 12. It will also be appreciatedthat each individual member 8 may enter into a number of contacts withthe provider of the tool 12. The contract manager 306, for example, isoperable to store the terms of each contract and party-specificinformation in the tables 24. The contract manager 306 is adapted toinstruct the purchasing manager 302 to sell or unwind futures contractsin each delivery month.

Reference is next made to FIG. 14, which illustrates the series of stepsperformed by the purchasing manager 302 when a member 8 chooses to lockin the predicted future prices 910 for a selected cut of meat 506 for aselected delivery period. At step 660, the purchasing manager 302retrieves the hedge ratio 906 from the prediction model 204 via thepredictor module 200. The hedge ratio 906 is used by the purchasingmanager at step 662 to determine the quantity of live cattle futures topurchase to hedge against future price movements in the selected cut. Atstep 664, the purchasing manager 302 accesses a trading system 308 on afutures market 310 and buys the appropriate number of futures at step668. Finally, at step 670, the purchasing manager 302 instructs the datamanager 34 to record the number of futures purchased, the price paid forthe futures, the type and quantity of the cut of meat and the selecteddelivery month in the tables 24 or directly in memory 40.

Alternative Embodiment of the Tool 12

Reference is next made to FIG. 15, which shows a component diagram of analternative embodiment of the portfolio management tool 12. In additionto the functionality described above, the portfolio management tool 12is operable to lower the overall risk of the aggregate portfolio 960 ofthe provider of the tool 12 as the provider guarantees forward prices ofvarious cuts of meat to customers 8. As shown, the management tool 12includes a receipt module 30 for managing user requests 9 and fordirecting user requests to one or more of the appropriate modules 32,200, 300, 400, 540 and/or 560. As the member 8 interacts with the visualrepresentation 28 via input devices 38, the receipt module 30co-ordinates the responsibilities and tasks of the other modules 32,200, 300, 400, 540 and/or 560 of the management tool 12. The managementtool 12 communicates with a data manager 34 which is operable toretrieve data from and send data to the tables 24 upon instruction fromthe receipt module 30 and/or the other components of the tool 12. Thedata manager 34 is also in electronic communication with the derivativedata provider 18 and the meat data provider 20. The visual manager 36 isinstructed by the components of the management tool 12 to recreate andredraw the visual representation 28 for viewing and further interactionby the user or member 8. The tool 12 includes a model module 560 forcreating the model(s) 204 of the portfolio management tool 12. Asdescribed below, the model module 560 is operable to create an models204 for representing each cut of meat and each corresponding deliverytime period that is offered to the member 8 by the tool 12. The modelmodule 560 instructs the data manager 34 to store each model 204 in thetables 24 or directly into memory 40 of the electronic device 18 for useby the other components of the tool 12. The tool 12 also includes apredictor module 200 which is operable to generate the future predictedprices of a cut of meat for a selected period upon selection by the user8. The predictor module 200 is in communication with and retrieves theappropriate model from the model module 560. The predictor module 200uses the model 204 to create a prediction table 220 and instructs thevisual manager 36 to render the prediction table 220 to the visualinterface 28 for viewing and interaction by the member 8. The tool 12also includes a Purchasing Module 300 for buying and selling live cattlefutures contracts on a futures market 310. The purchasing module 300retrieves a hedge ratio 906 from the model module 560 and determines thenumber of live cattle futures to buy. Once the number of live cattlefutures is determined by the purchasing module 300, components of thepurchasing module 300 buy the appropriate number of live cattle futuresdetermined by the hedge ratio 906 and store the information regardingthe trade in the tables 24 via the data manager 34. The tool alsoincludes a subscription manager 32 for managing the member accounts ofthe members 8 and for instructing the visual manager 36 the type ofinformation to display to the member 8 and the types of interaction thatthe member 8 has permission to perform. In addition, the tool 12includes an optimization module 400. The optimization module 400 is forcalculating correlation factors for each pair of assets in the aggregateportfolio 960 and for creating optimization decisions 404. The decisions404 are used by the adjustment module 540 to alter the internalstructure of the models 204 to make some cuts of meat more attractive(i.e. less expensive) relative to other cuts of meat (which may be mademore expensive) for customers 8 and potential customers of the tool 12.The adjustment module 540 may communicate adjustment parameters to themodel module 560 which modifies the appropriate model 204 uponinstruction by the adjustment module 540. Alternatively, the adjustmentmodule 540 directly modifies models 204 to put some cuts of meat on saleand to increase the prices of other cuts of meat. By continuallygenerating optimization decisions 404 and modifying the internalstructure of models 204, the risk associated with guaranteeing forwardprices of cuts of meat in an aggregate portfolio 960 is continuallybeing monitored and optimized for the provider of the tool 12.

Aggregate Portfolio 960

Reference is next made to FIG. 16, which illustrates an aggregateportfolio 960 of the provider of the tool 12. The aggregate portfolio960 includes customer portfolios 962 a-n which are held by the members 8a-n. The aggregate portfolio 960 therefore includes all cuts of meat foreach delivery period for each customer 8 of the tool 12. The provider ofthe tool 12 is exposed to risk by securing the forward prices 910 of thecuts of meat in the portfolios 962 a-n for each of the customers 8 a-n.It will be appreciated that the provider of the tool 12 is desirous oflowering its overall risk exposure of the aggregate portfolio 960 whilestill profiting from offering the services of the tool 12.

Optimization Module 400

Reference is next made to FIG. 17, which illustrates a PortfolioOptimization Module 400 of the management tool 12. The portfoliooptimization module 400 includes an Optimization manager 402 which isoperable to generate optimization decisions 404 based on the aggregateportfolio content 960 resident in the tables 24. The optimizationmanager 402 performs statistical analysis on the relationship betweenhistoric market prices of pairs of cuts of meat in the portfolio. Theoptimization decisions 404 are designed to reduce the risk of theprovider of the portfolio management tool 12 in securing the predictedfuture prices 910 of selected cuts of meat 506 for selected deliveryperiods. For example, the manager 402 may calculate that based onhistorical data, one cut of meat is negatively correlated with anothercut of meat in the same month, for example meaning that one cut of meatis found to historically increase in price while another cut of meathistorically decreases, such that the variation in the historical pricesrepresents price variation in the historical prices. This negativecorrelation also means that the price variation of one meat cuts isopposite to the price variation of the other meat cut (e.g. one meat cutprice increases while the other meat cut price decreases).

The manager 402 then generates decisions 404 that are implemented bymodules of the tool 12 to increase the quantity of one or both cuts ofmeat in the portfolio 960, thus lowering the overall risk of theportfolio 960 as is known. Likewise, the manager 402 may generatedecisions 404 to decrease the quantity of positively correlated cuts ina portfolio 960. The optimization manager 402 may periodically generateoptimization decisions 404 at a predetermined frequency or theoptimization manager 402 may dynamically generate decisions 404 on areal-time basis as the content of the aggregate portfolio 960 ismodified (i.e. as members 8 choose to secure the future prices 910 ofselected cuts 506 in exchange for paying the basis risk premium 916and/or as the delivery periods of selected cuts 506 expire). In anembodiment, the manager 402 generates decisions 404 to alter the models204, for example, to lower the basis risk premium charged on a cut ofmeat, effectively putting the cut of meat on sale. In addition, thedecisions 404 may be used by employees of the provider of the tool 12 totarget certain customers that may be interested in certain cuts of meat.It is also recognised that that one of the meat cuts may be alreadypresent in the portfolio 960 while another of the meat cuts may not yetbe included in the portfolio 960. In this case, the portfolio 960content may be optimized/adjusted by looking for and attempting toinclude those cuts of meat that have opposite price variations to thosemeat cuts already present in the portfolio 960, for example.

The optimization module 400 provides several advantages by generatingoptimization decisions 404, such as:

-   -   It reduces short-term exposure of the provider of the tool 12 to        basis risk 912. The more cuts in a portfolio 960, the greater        the likelihood that a high basis risk 912 on one cut will be        offset by a low basis risk 912 on another cut,    -   It creates an opportunity for arbitrage by aggregating different        customers with different individual exposures into a lower risk        portfolio 960, while still collecting basis risk premiums 916        priced to individual cuts. The weighted average sum of the basis        risk premiums 916 charged on individual cuts is greater than the        basis risk premium 916 that would be required for the overall        portfolio 960 as demonstrated by table 420 and further described        below.

It is known that diversification in an aggregate portfolio 960 whichincludes different types of assets can be quantified by anintra-portfolio correlation. This is a statistical measurement betweennegative one and positive one that measures the degree to which thevarious assets in a portfolio 960 can be expected to perform in asimilar fashion or not. A measure of −1 means that the assets within theportfolio 960 perform perfectly oppositely: whenever one asset goes up,the other goes down. A measure of 0 means that the assets fluctuateindependently, i.e. that the performance of one asset cannot be used topredict the performance of the others. A measure of 1, on the otherhand, means that whenever one asset goes up, so do the others in theportfolio. To eliminate diversifiable risk completely, one needs anintra-portfolio correlation of −1. Prices of cuts of meat may move inthe same direction depending on the price of live cattle; however, if aprediction model 204 of a first cut generates a positive error or basis(i.e. the model predicts higher prices 910 than occur on the market), ithas been found that the prediction model 204 of a second cut that isnegatively correlated with the first cut will generate a negative basis.In this way, a provider of the tool 12 is able to offset any losses fromthe first cut by increasing the quantity of the second cut in theportfolio 960.

The price of live animal (e.g. cattle) can be a significant factor thatinfluences the market prices of cuts of meat. The expected price of livecattle for a period in the future is reflected in the price of livecattle futures. When live cattle goes up in price by a significantpercentage, most if not all cuts of meat will follow the trend in theprice of live cattle. In times of relative price stability in livecattle or when there is no discernible trend in the price of livecattle, however, the demand for a particular cut of meat may be a moredominant factor that influences the price of a particular cut of meatthan the price of live cattle. Market demand itself may be influenced byseveral factors such as the season of the year or the economicprosperity of a given market. When prices of live cattle are relativelystable a pair of cuts of meat may be negatively correlated with eachother, such that one of the cuts of meat in the pair is not followingthe movement in the price of live cattle futures. The optimizationmanager 402 can calculate an intra-portfolio correlation Q for each pairof cuts of meat in the portfolio 960 that share the same deliveryperiod. The provider of the tool 12 sets pre-determined thresholds ofpositive and negative correlations such that decisions 404 are createdby the optimization manager when negative correlations are at or below apre-determined threshold and the positive correlations are at or above apre-determined threshold. For example, in times of relative stability inthe price of live cattle, the optimization manager 402 may determinethat many pairs of cuts of meat are negatively or positively correlatedwith each other (and below or above the pre-determined thresholdsrespectively) giving the provider of the tool many options to optimizethe portfolio 960 based on the decisions 404 created by the manager 402.As mentioned above, the models 204 that correspond to negativelycorrelated cuts of meat may be altered to effectively put the cuts ofmeat on sale and the models 204 that correspond to positively correlatedcuts of meat may be altered to effectively make the cuts of meat moreexpensive. The adjustment module 540 may adjust the price of the basisrisk premiums 916 up or down to make a cut more expensive or lessexpensive respectively.

As mentioned above, when the price of live animal (e.g. cattle)increases by a significant percentage and/or when the price of livecattle has a discernible trend, most if not all cuts of meat will changeprices in the same direction of the price of live cattle. In thissituation, most if not all pairs of cuts of meat will be positivelycorrelated. In another embodiment of the tool 12, the optimizationmanager 402 can be operable to calculate a different intra-portfoliocorrelation factor Q than the one described above. The manager 402analyzes the past predicted prices of each pair of cuts of meat for thesame delivery period, for example, in the portfolio 960 and determinesthe basis (i.e. difference between actual market price and predictedprice) in the predicted prices. The manager 402 then calculates anintra-portfolio correlation on the basis for the first cut of meat andthe basis on the second cut of meat in the pair. The manager 402 createsdecisions 404 that may be used by the adjustment module 540 when thecorrelation is above or below the pre-determined thresholds. Forexample, the manager 402 may determine that a first cut of meathistorically has a negative basis for the delivery month and the secondcut of meat historically has a positive basis for the delivery month(i.e. the model 204 of the first cut historically predicts prices abovethe respective market price and the model 204 of the second cuthistorically predicts prices below the respective market price). If thecorrelation between the first cut and the second cut is below thepre-determined threshold, the adjustment module 540 may adjust themodels 204 (for example, by decreasing the basis risk premiums 916)corresponding to the first and second cuts to make the cuts lessexpensive. If the correlation between a first and second cut is positiveand above a pre-determined threshold, the adjustment module 540 mayadjust the models 204 (for example, by increasing the basis riskpremiums 916) corresponding to the first and second cuts to make thecuts more expensive

In an embodiment, the optimization module 400 employs a value-at-riskmodel that uses the technique known as intra-portfolio correlation togenerate optimization decisions 404. The optimization manager 402retrieves portfolio content 960 from the tables 24 by sending a requestto the data manager 34. The aggregate portfolio content 960 is anaggregate collection of all prices, quantities, delivery periods and thetypes of meat for which the provider is providing future prices tomembers 8, as well as perhaps other information. The optimizationmanager 402 analyzes the aggregate portfolio content 960 and calculatesthe positive or negative correlation for pairs of different cuts of meatin the provider's portfolio 960. As is understood, optimizing aportfolio requires holding cuts of meat with poor (or offsetting)correlations. As mentioned above, if the prediction 910 of one cut has apositive basis then there is a higher likelihood that a poorlycorrelated cut will have a negative basis. When the provider of the tool12 agrees to guarantee a forward price 910 for a particular cut to acustomer 8, the optimization manager 402 will analyze which cuts have apoor correlation and either target customers who would be interested inforward prices 910 on those cuts and/or adjust the basis risk premiumconfidence level on those cuts down (analogous to putting those cuts “onsale”). Optimization decisions 404 may be in the form of a list ofoptions which the provider can select to optimize the aggregateportfolio 960, and hence, lower the risk of the provider in providingforward prices 910. For example, the list may include an option toincrease the basis risk premium confidence level 918 to one of severalvalues, or to leave the basis risk premium confidence level 918 at thesame value. Alternatively, optimization decisions 404 may be executeddirectly by the optimization manager 402 to modify the basis riskpremium confidence levels 918 of models 204 that correspond to cuts ofmeat that are poorly correlated.

In one embodiment, the optimization module 400 employs anintra-portfolio correlation that is represented by the followingformula:

$Q = {\left( {\sum\limits_{i}\; {\sum\limits_{j}\; {X_{i}X_{j}p_{ij}}}} \right)/\left( {\sum\limits_{i}\; {\sum\limits_{j}\; {X_{i}X_{j}}}} \right)}$

Where Q is the intra-portfolio correlation,

X i is the fraction invested in asset i,

X j is the fraction invested in asset j,

P ij is the correlation between assets i and j,

The expression is only computed when i≠j

In the above equation, it is noted that there is a uniqueintra-portfolio correlation for every pair of assets in a particulardelivery period. For example, if the provider of the tool 12 isguaranteeing the forward prices 910 of rib-eyes for December,strip-loins for December and rib-eyes for February of the same year inits aggregate portfolio 960, there will be a correlation factor Q onlyfor the pair made up of rib-eyes for December and strip-loins forDecember. The optimization manager 402 does not compute a correlationfactor for rib-eyes for February with the other assets because of theseasonal nature of cuts of meat. That is, it is not expected thatrib-eyes for February are statistically correlated with cuts of meat formonths other than February. It is to be understood that in otherembodiments of the tool 12, however, a year may be divided up intoseasons instead of months. In this embodiment, there is a correlationfactor Q for every cut of meat that shares a delivery season. Forexample, the manager 402 will calculate one correlation factor forrib-eyes with strip-loins that are to be delivered in Spring, but notfor rib-eyes to be delivered in Spring with strip-loins to be deliveredin Fall. To calculate the correlation factor Q for a given pair ofassets in the same delivery period, the optimization manager 402 takestwo vectors of historic data as inputs and calculates the correlationcoefficient Pij: one vector represents data for the first cut of meatfor the delivery period, the second vector represents data for thesecond cut of meat for the delivery period. For example, the provider ofthe tool may use 8 years of data for the delivery month to calculate Q.In this example, the first vector contains average prices of the firstcut for the delivery month in each year and the second vector containsaverage prices of the second cut for the delivery month in each year. Asis known, the correlation coefficient Pij indicates the strength anddirection of a relationship between two random variables. Thecorrelation coefficient implemented by the optimization manager 402 maybe a known correlation coefficient that uses covariance or otherstatistical properties or may be a customized correlation coefficientdeveloped by the provider that is specific to the tool 12. In anotherembodiment of the tool 12, in calculating the correlation factor Q, thefirst input vector contains the average daily prices for each day in thedelivery month for the first cut, and the second vector contains theaverage daily prices for each day in the delivery month for the secondcut. It will be understood that the input vectors may include sampledaily prices for each day, one specific day or a group of days in thedelivery month for the first and second cuts respectively.

In operation, the optimization manager 402 retrieves the aggregateportfolio content 960 from the data manager 34 as an input. For eachpair of assets in the portfolio (i.e. the cuts of meat in the samedelivery month for which the provider has secured forward prices 910),the optimization manager 402 generates an intra-portfolio correlationfactor. The correlation factor for the asset pair represents therelative movements of one cut of meat in the asset pair relative to theother cut of meat in the asset pair for the particular delivery month.The optimization manager 402 is operable to analyse the set ofintra-portfolio correlations and generate optimization decisions 404. Inan embodiment, the manager 402 searches for intra-portfolio correlationsthat are negative and below a pre-determined threshold (−0.5 forexample). The manager 402 produces decisions 404 that are designed toincrease the content of the portfolio 960 made up of asset pairs belowthe negative pre-determined threshold. The manager. 402 also searchesfor portfolio correlations that are positive and above a pre-determinedthreshold (+0.5 for example) and produces decisions 404 designed todecrease the content of the portfolio 960 devoted to the asset pairsabove the pre-determined threshold. It is to be understood that thepre-determined thresholds are set by the provider of the tool 12 and maybe changed by the provider at any time. As mentioned above, thedecisions 404 may be used to modify the models 204 of cuts of meat suchthat asset pairs with negative correlations are put on sale and assetpairs with positive correlations are made more expensive.

In one embodiment, the optimization manager 402 uses pre-determinedsetting to determine if both cuts of meat in a pair with offsettingcorrelations should be put on sale or if only one of the cuts and whichone of the cuts of meat should be put on sale. The settings arecustomizable by the provider of the tool 12. For example, a provider ofthe tool 12 may create electronic settings that instruct the manager 402to put one cut of meat in the pair on sale at a discount equal to twiceas much of the other cut of meat in the pair, either in absolutemonetary terms or as a percentage of the predicted future price 910. Itis to be understood that the provider may create such settings for eachcut of meat in the portfolio relative to each of the other cuts of meatin the portfolio. In another aspect, an employee of the provider of thetool makes this decision after prompting by the manager 402 on a userinterface 28. For example, the employee may wish to consider otherinformation such as business trends, competitive strategy, short-termweather predictions or any other information relevant to the business ofthe provider of the tool 12.

In one embodiment, the optimization manager 402 communicates theoptimization decisions 404 to an adjustment module 540 which directlyadjusts the internal structure of models 204 to put certain cuts of meaton sale and to make other cuts of meat more expensive.

Adjustment Module 540

Referring next to FIG. 18, an adjustment module 540 of the tool 12 isillustrated. The adjustment module 540 receives optimization decisions404 from the optimization model 400. Optimization decisions 404 may beexecutable electronic instructions to be executed by the adjustmentmodule 540 to adjust models 204 or may be in the form of a data set thatis retrievable by the adjustment module 540 via the data manager 34. Theadjustment module 540 reads and/or executes the optimization decisions404 and modifies the internal structure of models 204. For example, theoptimization manager 402 may determine that a first cut of meat in theportfolio is well correlated with a certain cut, that a second cut ofmeat in the portfolio is poorly correlated with the certain cut andthere is no correlation between the first cut and the second cut. Themanager 402 creates optimization decisions 404 to decrease the quantityof the first cut in the portfolio 960 and to increase the quantity ofthe second cut in the portfolio 960. The adjustment module 540 receivesthe optimization decisions 404 as an input and adjusts the models 204that correspond to the first and second cuts respectively. Specifically,to reduce the overall risk of the portfolio 960, the adjustment module540 adjusts the model 204 of the first cut to provide a disincentive toprospective customers by increasing the basis risk premium 916 a of thefirst cut. The provider has several options for increasing the basisrisk premium 916 a of the first cut. The adjustment module 540 mayincrease the basis risk premium confidence level 918 a of the first cutfrom 80% to 90%, meaning that a customer must pay an amount of moneyequal to a 40% risk of the forecast to secure a predicted price 910.Alternatively, the adjustment module 540 may directly increase the basisrisk premium 916 a of the first cut to a predefined level, making thefirst cut of meat more expensive than it was prior to modification bythe adjustment module 540. Additionally, the adjustment module 540decreases the basis risk premium confidence level 918 b of the secondcut, which lowers the basis risk premium 916 a of the second cut,effectively putting the second cut on sale. For example, the adjustmentmodule 540 may decrease the basis risk premium confidence level 918 b onthe second cut to 70%, meaning that a member 8 must only pay an amountequal to a 20% risk of the forecast to secure a predicted price 910.Alternatively, the adjustment module 540 may directly decrease the basisrisk premium 916 b on the second cut. It is expected that over time,putting cuts of meat with offsetting correlations on sale will decreasethe overall risk that the provider of the tool 12 is exposed to insecuring the predicted prices 910 of the cuts of meat in the aggregateportfolio 960. Likewise, making cuts of meat with positive correlationsmore expensive will also decrease the risk exposure of the provider ofthe tool 12 in guaranteeing forward prices 910 for cuts of meat in theaggregate portfolio 960.

Reference is next made to FIG. 19, which illustrates the series of stepsperformed by the optimization manager 402 and the adjustment module 540in optimizing the risk level associated with the portfolio 960 of theprovider of the tool 12. At step 800, the optimization manager 402calculates the intra-portfolio correlation of each pair of cuts of meatthat share a delivery month in the portfolio 960. At step 802, theoptimization manager 402 analyzes each correlation term and determinesif the correlation terms are above or below a pre-determined threshold.For example, the provider may customize the optimization module 400 suchthat the optimization manager 402 creates optimization decisions 404 foreach correlation term above +0.5 and below −0.5. At step 804, theoptimization manager 402 creates the appropriate optimization decisions404 for each correlation term that is above or below the predeterminedthresholds. The optimization decisions 404 are directed to theadjustment module 540 at step 806. At step 808, the adjustment module540 retrieves the models 204 that correspond to the optimizationdecisions 404 and modifies the models 204 as described above, to eitherincrease or decrease the basis risk premium 916 on the relevant cuts ofmeat.

Reference is next made to FIG. 20, which illustrates the advantageousnet effect of the operations performed by the optimization module 400and the adjustment module 540 on the risk level of the aggregateportfolio 960 held by the provider of the portfolio management tool 12.The table 940 shows the content of the portfolio 960 (i.e. by cut ofmeat 506 and the percentage 942 of the portfolio 960 made up of eachcut), the individual basis risk premium 916 for each cut of meat, theweighted average basis risk premium 944 and the aggregate basis riskpremium 946 of the aggregate portfolio 960. The basis risk premium 944is a weighted average of the basis risk premiums 916 as the selectedcuts 506 were sold individually to different members 8. As shown, theweighted average of the basis risk premiums 944 charged for guaranteeingfuture prices 910 of those cuts of meat to members 8 is $0.1569. Thatis, the provider of the tool 12 has collected an average basis riskpremium 944 of $0.1569 per cut 506 as the provider has built up theportfolio 960. However, the basis risk premium 946 that would have beenrequired if the cuts of meat 506 were all sold to one customer is only$0.0995. The provider of the tool is able to realize a net benefit 948of +$0.0574 on each cut of meat, which represents the benefit ofexecuting the optimization decisions 404 of the optimization module 400.The net benefit 948 to the provider of the tool 12 is realized bycreating an aggregate portfolio 960 of pairs of assets with offsettingcorrelation values by adjusting the basis risk premium confidence levels918 of the assets as described above. The optimization decisions 404provide an incentive to members 8 to buy cuts of meat that lower therisk exposure of the provider and a disincentive to members 8 to buycuts of meat that increase the risk exposure of the provider of the tool12.

As an example, the percentage of a portfolio 960 a (not shown) devotedto the cut ‘90s’ is 20% and the percentage of the portfolio 960 adevoted to ‘50s’ is 10%. The remaining percentage of the portfolio. 960a is made up of other cuts not relevant to the example. The optimizationmanager 402 determines that asset pairs ‘90s’, ‘50s’ are negativelycorrelated and below a pre-determined threshold. In portfolio 960 a, thenet benefit 948 a is lower than the net benefit 948 of +$0.0574 in Table940. The manager 402 generates decisions to put ‘90s’ and ‘50s’ on saleby lowering the basis risk premium 916 on each of the cuts. The basisrisk premium 916 on the other cuts of meat in the portfolio 960 a mayalso be modified or may stay the same depending on the provider. Inaddition, because the provider of the tool knows the portfoliocorrelation between ‘90s’, ‘50s’, the provider may target customers 8that are interested in these cuts. Over time, members 8 buy more of‘90s’ and ‘50s’ eventually leading to the provider having the portfolio960 in Table 940 made up of 32% of ‘90s’ and 18% of ‘50s’. The providernow realizes a greater net benefit 948 of +$0.574 in the portfolio 960as compared to the net benefit 948 a in the portfolio of 960 a. The netbenefit 948 of the portfolio 960 is greater than that of portfolio 960 abecause the portfolio 960 is less risky to the provider of the tool 12,yet the provider of the portfolio 960 is able to charge individual basisrisk premiums 916 that do not reflect the offsetting correlations in thecuts of meat in the portfolio 960.

It will be appreciated that another set of optimization decisions 404may result in a higher or lower net benefit 948 to the provider of thetool 12 and that other diversification strategies are usable by the tool12 in lowering the risk exposure in the aggregate portfolio 960. Forexample, in another embodiment, the optimization decisions 404 are notdirectly executed by the adjustment module 540, but instead, are used bythe provider of the tool 12 for analysis only. The provider of the tool12 may choose to implement the optimization decision 404 as describedabove or may choose to disregard the optimization decisions 404. In yetanother embodiment, the provider of the tool 12 can set a maximumpercentage for each cut of meat that can be included in the aggregateportfolio 960. For example, a provider of the tool 12 may wish to limitthe content of the portfolio 960 devoted to rib-eyes to 20%, or anyother percentage. Once 20% of the portfolio content is devoted torib-eyes, customers 8 no longer have the ability to secure forwardprices 910 of rib-eyes until rib-eyes make up less than 20% of theaggregate portfolio 960.

1. A method for determining a future price of a selected meat cut type(MCT) of an animal for a selected future time period (FTP), the methodincluding the steps implemented on a computer processor of: receivingthe selected MCT; storing said selected MCT in a memory; using a pricemodel configured for determining said future price of said selected MCTfor the selected FTP, said future price based on one or more definedrisk levels, historical market price of said selected MCT for one ormore time periods prior to said selected FTP, and live animal futuresdefining a price of the live animal traded as a commodity; determiningvia the model the future price of the selected MCT for the selected FTP,a price premium for the selected MCT for the selected FTP, and a hedgerelationship defining the relative price of the live animal futures withsaid future price for the selected FTP; and sending the future price andthe price premium for the selected MCT for the selected FTP forpresentation on a user interface.
 2. The method of claim 2 furtherincluding the step of including an error term in the price model, theerror term defined as a relationship between a historical value of apreviously determined future price for an earlier time period prior tothe selected FTP and a historical market price of the selected MCT forthe earlier time period.
 3. The method of claim 2, wherein the errorterm is a residual term of the price model for the earlier time period.4. The method of claim 2, wherein the error term includes two residualsof the price model corresponding to two earlier time periods prior tothe selected FTP.
 5. The method of claim 4, wherein the selected FTP isa month and the corresponding two earlier time periods are two monthsprior to the selected FTP.
 6. The method of claim 2, wherein the pricemodel includes input parameters selected from the group comprising: thelive animal future for one or more time periods prior to the selectedFTP; the live animal future during the selected FTP; the live animalfuture after the selected FTP; and the risk levels of a defined basisrisk level and a defined confidence level.
 7. The method of claim 2,wherein the price model is configured as a linear regression model basedon the live animal futures and historical MCT prices for one or moretime periods prior to the selected FTP.
 8. The method of claim 2,wherein one or more of the price models are provided for determining thefuture price of the MCT for each of a plurality of corresponding FTPs.9. The method of claim 3, wherein the animal is selected from the groupconsisting of: cattle and hogs.
 10. The method of claim 2, wherein thehedge relationship is a ratio between the price of the live animalfuture and said future price for the selected FTP.
 11. The method ofclaim 2, wherein the error term is a difference between the historicalvalue of the previously determined future price and the historicalmarket price of the selected MCT for a time period earlier than theselected FTP for a specified level of the live animal future.
 12. Themethod of claim 2, wherein the selected FTP is selected from the groupconsisting of: a month; a quarter of a year; a selected combination ofmonths; a week; and a day.
 13. The method of claim 12, wherein theearlier time period is selected from the group consisting of: a month; aquarter of a year; a selected combination of months; a week; and a day.14. The method of claim 1 further comprising the step of receiving anorder for a quantity of the selected MCT for the determined future priceand purchasing an associated quantity of at least one live animalfutures contract based on the determined hedge relationship for theselected MCT.
 15. The method of claim 14 further comprising the step ofselling the associated quantity of the at least one live animal futurescontract at a settlement time period based on the selected FTP.
 16. Themethod of claim 15, wherein the settlement time period is prior toreaching the selected FTP.
 17. The method of claim 15, wherein thesettlement time period is after reaching the selected FTP.
 18. Themethod of claim 15, wherein the settlement time period is the same asthe selected FTP.
 19. The method of claim 8 further comprising the stepof receiving an order for a quantity of the selected MCT distributedover two of the selected FTPs and purchasing an associated quantity of alive animal futures contract for each of the two selected FTPs based onthe respectively determined hedge relationships for each of the selectedFTPs.
 20. The method of claim 19 further comprising the step of sellingeach of the quantities of the live animal futures contracts at arespective settlement time period based on the respective selected FTP,such that the respective settlement time period is different for each ofthe two selected FTPs.
 21. A system for determining a future price of aselected meat cut type (MCT) of an animal for a selected future timeperiod (FTP), the system including: a computer processor; a receiptmodule for receiving said selected MCT; a memory for storing saidselected MCT; a predictor module configured for using a price model todetermine said future price of said selected MCT for the selected FTP,said future price based on one or more defined risk levels, historicalmarket price of said selected MCT for one or more time periods prior tosaid selected FTP, and live animal futures defining a price of the liveanimal traded as a commodity; the predictor module further configuredfor determining via the price model the future price of the selected MCTfor the selected FTP, a price premium for the selected MCT for theselected FTP, and a hedge relationship defining the relative price ofthe live animal futures with said future price for the selected FTP; anda presentation module for sending the future price and the price premiumfor the selected MCT for the selected FTP for presentation on a userinterface.
 22. The system of claim 21, wherein the price model furtherincludes an error term in the price model, the error term defined as arelationship between a historical value of a previously determinedfuture price for an earlier time period prior to the selected FTP and ahistorical market price of the selected MCT for the earlier time period.23. The system of claim 22, wherein one or more of the price models areprovided for determining the future price of the MCT for each of aplurality of corresponding FTPs.
 24. The system of claim 21 furthercomprising a purchase module for receiving an order for a quantity ofthe selected MCT for the determined future price and for purchasing anassociated quantity of at least one live animal futures contract basedon the determined hedge relationship for the selected MCT.
 25. Thesystem of claim 24, wherein the purchase module is further configuredfor selling the associated quantity of the at least one live animalfutures contract at a settlement time period based on the selected FTP.26. The system of claim 23 further comprising a purchase module forreceiving an order for a quantity of the selected MCT distributed overtwo of the selected FTPs and for purchasing an associated quantity of alive animal futures contract for each of the two selected FTPs based onthe respectively determined hedge relationships for each of the selectedFTPs.
 27. The system of claim 26, wherein the purchase module is furtherconfigured for selling each of the quantities of the live animal futurescontracts at a respective settlement time period based on the respectiveselected FTP, such that the respective settlement time period isdifferent for each of the two selected FTPs.